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        }
      ],
      "xlabel": "Observations per region m",
      "ylabel": "Variance of regional mean",
      "logx": true,
      "logy": true
    },
    {
      "kind": "bar",
      "labels": [
        "Area weighted",
        "Naive sample"
      ],
      "series": [
        {
          "label": "Mean biomass (constructed units)",
          "y": [
            200,
            120
          ]
        }
      ],
      "ylabel": "Target / sample mean"
    }
  ],
  "title": "Within-region density cannot remove between-region uncertainty",
  "assumptions": "Region variance4, within-region variance100, independent balanced sampling. Two forest types100/300; actual area weights0.5/0.5, sample weights0.9/0.1. No GEDI measurements.",
  "worked": "两类森林100、300且面积各半时总体均值200；样本90%来自低值类时朴素均值120。J=20时方差0.2+5/m，增加m无法消除0.2。",
  "checks": {
    "weighted_mean": 200.0,
    "naive_mean": 120.0,
    "variance_J20_m100": 0.25
  },
  "generator_source_sha256": {
    "ops/datacenter/editorial_guides/horizons_figures.py": "5ed0031cf4cbcfb7d2d78b4385c8c29200a8312d53744507241da3ca5ca44a9c",
    "ops/datacenter/editorial_guides/deep_figures.py": "158969d9b1f5a4bd13906cf78dd71affc2692df9194593918059d861a6f59c85"
  },
  "source_ids": [
    "h-gedimodel2022",
    "h-gediinference2022"
  ],
  "formula_source": "Article equations and this versioned script",
  "diagram_nodes": [
    "Lidar footprints",
    "Field calibration",
    "Spatial validation",
    "Area weighting",
    "Joint uncertainty",
    "Stock-change inference"
  ]
}